{"type":"rich","version":"1.0","provider_name":"Transistor","provider_url":"https://transistor.fm","author_name":"Manufacturing Hub","title":"Ep. 277 - Inside the Siemens Pop Up Factory: How a Modular CPG Plant Runs Half Real and Half Virtual","html":"<iframe width=\"100%\" height=\"180\" frameborder=\"no\" scrolling=\"no\" seamless src=\"https://share.transistor.fm/e/b5f12b7d\"></iframe>","width":"100%","height":180,"duration":4235,"description":"The Siemens pop up factory is a modular CPG plant where virtual PLCs, real controllers, and a live digital twin run small batches side by side.\nMost factories are built for long runs of the same product. Davide Picciotto explains why that model is being stretched. COVID supply chain shocks pushed brands to add flexible areas inside existing plants, and events like the World Cup raised a sharper question: what if a factory could be stood up close to demand, run personalized products for a season, and then move? Siemens is validating the idea with PepsiCo through what Davide calls a hybrid immersive prototype: half the factory is virtual, half is real, and both run as one system. Pininfarina, the design house behind decades of Ferraris, designed the modular shell.\nAndrea Sassetti walks through the live environment that debuted at Hannover Messe. Four PLCs, some physical and some virtual on Industrial Edge, feed the Industrial Information Hub, which adds a semantic layer and pushes data to the cloud. Virtual PLCs also simplify firmware and security patching. A capability matching engine reads each recipe step, checks which equipment is available and stocked with the right material, and routes the batch in real time. The same engine builds rainbow pallets of mixed product layers, using the Simatic robot library over SRCI so the same code runs on a Universal Robots cobot or any compatible arm.\nThe most interesting idea is in the mixer. Traditionally, mix time and speed are hard coded into the recipe. Siemens ran thousands of Simcenter fluid dynamics simulations across viscosities, particle sizes, and impeller types, trained a model in AI Studio from the Altair RapidMiner acquisition, and deployed it on Industrial Edge. The recipe now specifies material properties, and the model infers setpoints on the fly, even when an ingredient comes from a new supplier.\nDavide closes on what is still missing: digital threads between engineering and execution remain incomplete, with...","thumbnail_url":"https://img.transistorcdn.com/yoKAvzBXZ3YjQTekFk7KFGXeuwJ29WgXvop3dVEfhLs/rs:fill:0:0:1/w:400/h:400/q:60/mb:500000/aHR0cHM6Ly9pbWct/dXBsb2FkLXByb2R1/Y3Rpb24udHJhbnNp/c3Rvci5mbS9zaG93/LzE3MjEzLzE2MDk0/MzA1OTgtYXJ0d29y/ay5qcGc.webp","thumbnail_width":300,"thumbnail_height":300}